Discovering Diagnostic Gene Targets and Early Diagnosis of Acute GVHD Using Methods of Computational Intelligence over Gene Expression Data

Authors:
Maurizio Fiasché;Anju Verma;Maria Cuzzola;Pasquale Iacopino;Nikola Kasabov;Francesco C. Morabito
Affiliations:
University "Mediterranea" of Reggio Calabria, DIMET, Reggio Calabria, Italy I-89100;KEDRI, Auckland University of Technology, Auckland, New Zealand;Regional Center of Stem Cells and Cellular Therapy, "A. Neri", Reggio Calabria, Italy 89100;Regional Center of Stem Cells and Cellular Therapy, "A. Neri", Reggio Calabria, Italy 89100;KEDRI, Auckland University of Technology, Auckland, New Zealand;University "Mediterranea" of Reggio Calabria, DIMET, Reggio Calabria, Italy I-89100
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

This is an application paper of applying standard methods of computational intelligence to identify gene diagnostic targets and to use them for a successful diagnosis of a medical problem - acute graft-versus-host disease (aGVHD). This is the major complication after allogeneic haematopoietic stem cell transplantation (HSCT) in which functional immune cells of donor recognize the recipient as "foreign" and mount an immunologic attack. In this paper we analyzed gene-expression profiles of 47 genes associated with allo-reactivity in 59 patients submitted to HSCT. We have applied 2 feature selection algorithms combined with 2 different classifiers to detect the aGVHD at on-set of clinical signs. This is a preliminary study and the first paper which tackles both computational and biological evidence for the involvement of a limited number of genes for diagnosis of aGVHD. Directions for further studies are outlined.